As noise reducing methods, there are two main methods. A first method is a method of smoothing a peripheral area of a concerned pixel. A second method is a method of detecting an edge area and dividing a process according to the detection result.
In an example of the first method, a noise reducing apparatus determines peripheral pixels to be averaged from concentration and contrast of input image data, and at the same time, determines weighting coefficients to the peripheral pixels. As a technique related to this, Japanese Patent Application Publication (JP-A-Heisei 3-054679) discloses “Image Processing Apparatus.” This image processing apparatus has an averaging section for averaging input image data and peripheral pixel data, an averaging process determining section for determining the peripheral pixels to be averaged from the concentration and contrast of the input image data, a weighting calculating section for calculating weighting of the averaged data and the input image data, and a coefficient determining section for determining weighting coefficients from the concentration and contrast of the input image data.
In an example of the second method, the image processing system divides an input image into an edge area and an edge inverted area other than the edge area, an area, performs smoothing on the edge inverted area, and then combines it with the edge area. As a technique related to this, Japanese Patent Application Publication (JP-A-Heisei 09-044654) discloses “Image Processing Apparatus, Image Processing Method, Noise Eliminating Apparatus, and Noise Eliminating Method.” In this related technique, an A/D converter converts an analog brightness signal into a digital brightness signal through A/D conversion, and the digital brightness signal is supplied to a differentiator, a histogram generator, and a coefficient memory. The differentiator calculates an absolute value of a first-order derivative value (derivative absolute value) of the digital brightness signal and supplies it to the histogram generator. The histogram generator counts a brightness level of a digital brightness signal, which has a smaller derivative absolute value than a predetermined threshold, among derivative absolute values, and generates a histogram of the brightness level for one screen. A histogram smoother performs smoothing on the histogram. A coefficient calculator calculates a uniform area coefficient based on the smoothed histogram, and supplies a coefficient table to the coefficient memory. The coefficient memory makes the uniform area coefficient correspond with each pixel position in a screen.
However, in a noise reducing method of assigning weight coefficients to peripheral pixels to be smoothed, there is a case where an edge becomes discontinuous or a case where the entire image is converted to an oil-painted image, which leads to a case where a subjective image quality may be spoiled.
Moreover, in a noise reducing method of detecting the edge area, there is a case where the noise cannot be fully removed because of erroneous determination of an edge pixel and a noise pixel.
The causes of these problems are in that a noise component included in an image is not estimated accurately in any noise reducing methods.
In conjunction with the above description, Japanese Patent Application Publication (JP-P2006-060286A) discloses “Block Noise Reducing Apparatus.” The block noise reducing apparatus reduces a block noise in a screen where a motion of an image is very large, by executing a smoothing process for a flat area of the image in the screen. The smoothing process means to remove high spatial frequency components from an image data. Since the flat area of the image does not have high spatial frequency components originally, the image data is not lost even if the smoothing process is executed.
Moreover, Japanese Patent Application Publication (JP-P2006-115268A) discloses “Block Noise Reducing Apparatus.” This block noise reducing apparatus includes a frame correlation determining section, a flat region detecting section, a high frequency region detecting section, a smoothing process section, and a processing region setting section. The frame correlation determining section determines a correlation of a decoded image signal between frames. The flat region detecting section detects as a flat region, an image region that a brightness difference between its pixels and peripheral pixels is small, among decoded image signals. The high frequency region detecting section detects as a high frequency region, an image region that includes high spatial frequency components from among decoded image signals. The smoothing process section executes the smoothing process on the decoded image signal for a predetermined region. The processing region setting section sets an image region where the smoothing process section should execute the smoothing process on the decoded image signal. At this time, the processing region setting section sets as a processing region, a region that is the flat region of the decoded image signal in a frame that has a low correlation between the frames at least and is not the high frequency region.